Recorded at the ML in PL 2019 Conference, the University of Warsaw, 22-24 November 2019.
Martin Jankowiak (Uber AI Labs)
Slides available at docs.mlinpl.org/conference/201...
Abstract:
Probabilistic models offer a compelling methodology for reasoning about an uncertain world. Programming languages are powerful tools for specifying deterministic computations. Their synthesis--probabilistic programming languages (PPLs)--promises a unified and (partially) automated approach to specifying and reasoning about complex models. We give an introduction to PPLs, with examples drawn from economics and natural science serving as motivation. For concreteness we illustrate all our examples using the Pyro PPL.
Relevant links:
pyro.ai/
eng.uber.com/oed-pyro-release/
An Introduction to Probabilistic Programming arxiv.org/abs/1809.10756
16 сен 2020
